The summary feature extractor that we propose is based on a previously published neural network architecture presented in [1]}. For an input time series realization \(x \in \mathbb {R}^{n_s}\)  (\(n_s > 256\) ), we get an \(n_f\) -dimensional feature vector given by
\(f_{\lambda }(x) = g_3\big (g_2\big (g_1(x)\big )\big ) \in \mathbb {R}^{n_f}~,\) 
